The "Essence" of Network Security: An End-to-End Panorama / / edited by Mohuya Chakraborty, Moutushi Singh, Valentina E. Balas, Indraneel Mukhopadhyay |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Springer Singapore, 2021 |
Descrizione fisica | 1 online resource (XXXV, 289 p. 97 illus., 74 illus. in color.) |
Disciplina | 005.8 |
Collana | Lecture Notes in Networks and Systems |
Soggetto topico |
Telecommunication
Machine learning Cooperating objects (Computer systems) Big data Communications Engineering, Networks Machine Learning Cyber-Physical Systems Big Data |
ISBN | 981-15-9317-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction to Network Security Technologies -- A Systematic Review of Digital, Cloud and IoT Forensics -- Blockchain Based Framework for Managing Customer Consent in Open Banking -- A Comprehensive Study of Pros and Cons on Implementation of Block-chain for IoT device Security -- Role of Cryptography in Network Security -- Detection of Malicious URLs using Deep Learning Approach -- Software Defined Network Vulnerabilities -- Demystifying Security on NDN: A survey of Existing Attacks and Open Research Challenges -- Anonymous Traffic Network. |
Record Nr. | UNINA-9910863276303321 |
Springer Singapore, 2021 | ||
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Lo trovi qui: Univ. Federico II | ||
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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy [[electronic resource] ] : SPIoT-2020, Volume 1 / / edited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
Descrizione fisica | 1 online resource (XXXI, 884 p. 221 illus., 150 illus. in color.) |
Disciplina | 620.00285 |
Collana | Advances in Intelligent Systems and Computing |
Soggetto topico |
Engineering—Data processing
Cooperating objects (Computer systems) Computational intelligence Machine learning Big data Data Engineering Cyber-Physical Systems Computational Intelligence Machine Learning Big Data |
ISBN | 3-030-62743-8 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910483068103321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
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Lo trovi qui: Univ. Federico II | ||
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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy [[electronic resource] ] : SPIoT-2020, Volume 2 / / edited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
Descrizione fisica | 1 online resource (XXXII, 863 p. 222 illus., 128 illus. in color.) |
Disciplina | 004.678 |
Collana | Advances in Intelligent Systems and Computing |
Soggetto topico |
Engineering—Data processing
Cooperating objects (Computer systems) Computational intelligence Machine learning Big data Data Engineering Cyber-Physical Systems Computational Intelligence Machine Learning Big Data |
ISBN | 3-030-62746-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910483082303321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
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Lo trovi qui: Univ. Federico II | ||
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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy : SPIoT-2020, Volume 2 / / edited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Springer International Publishing, 2021 |
Descrizione fisica | 1 online resource (XXXII, 863 p. 222 illus., 128 illus. in color.) |
Disciplina | 004.678 |
Collana | Advances in Intelligent Systems and Computing |
Soggetto topico |
Engineering—Data processing
Cooperating objects (Computer systems) Computational intelligence Machine learning Big data Data Engineering Cyber-Physical Systems Computational Intelligence Machine Learning Big Data |
ISBN | 3-030-62746-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910863143603321 |
Springer International Publishing, 2021 | ||
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Lo trovi qui: Univ. Federico II | ||
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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy : SPIoT-2020, Volume 1 / / edited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Springer International Publishing, 2021 |
Descrizione fisica | 1 online resource (XXXI, 884 p. 221 illus., 150 illus. in color.) |
Disciplina | 620.00285 |
Collana | Advances in Intelligent Systems and Computing |
Soggetto topico |
Engineering—Data processing
Cooperating objects (Computer systems) Computational intelligence Machine learning Big data Data Engineering Cyber-Physical Systems Computational Intelligence Machine Learning Big Data |
ISBN | 3-030-62743-8 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910863143703321 |
Springer International Publishing, 2021 | ||
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Lo trovi qui: Univ. Federico II | ||
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The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy : SPIoT-2021 Volume 2 / / edited by John Macintyre, Jinghua Zhao, Xiaomeng Ma |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
Descrizione fisica | 1 online resource (999 pages) |
Disciplina | 006.3 |
Collana | Lecture Notes on Data Engineering and Communications Technologies |
Soggetto topico |
Engineering - Data processing
Cooperating objects (Computer systems) Computational intelligence Big data Artificial intelligence Data Engineering Cyber-Physical Systems Computational Intelligence Big Data Artificial Intelligence |
ISBN | 3-030-89511-4 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Analysis of Sentiment Tendency of Tourists' Comments Based on Text Mining -- Analysis of Smart City Construction Based on 5G Data Technology -- Prediction of Stock Price Based on Artificial Intelligence Algorithm -- Variation Translation Strategy System of Intangible Cultural Heritage Based on Data Mining -- A Computer-aided Comparative Study on Grammatical Cohesion in Abstracts of Sci-tech Journal Papers by Chinese and American Scholars -- Computer Graphics and Image Software in Advertising Design -- Design and Research of Production Information Management System for Project Based Mechanical Manufacturing Enterprises -- Impact of Computer Network Technology on Regional Economic Development -- Chaos Algorithm of Electrical Control System Based on Neural Network Technology -- Pulse Signal Acquisition System Based on Match Pursuit Algorithm -- Data Analysis of Power System Engineering Construction Based on PPSO Algorithm -- Reactive Optimization of Power System Based on K-means Algorithm -- Design and Structure Analysis of Manipulator based on Acceleration Sensor -- Discussion on Decision Tree Algorithm in University Teaching Management System. . |
Record Nr. | UNINA-9910523725703321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy : SPIoT-2021 Volume 1 / / edited by John Macintyre, Jinghua Zhao, Xiaomeng Ma |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
Descrizione fisica | 1 online resource (1169 pages) |
Disciplina | 006.31 |
Collana | Lecture Notes on Data Engineering and Communications Technologies |
Soggetto topico |
Engineering - Data processing
Cooperating objects (Computer systems) Computational intelligence Big data Artificial intelligence Data Engineering Cyber-Physical Systems Computational Intelligence Big Data Artificial Intelligence |
ISBN | 3-030-89508-4 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Application of Artificial Intelligence in Arrangement Creation -- Automatic Segmentation for Retinal Vessel Using Concatenate UNet++ -- Experimental Analysis of Mandarin Tone Pronunciation of Tibetan College Students for Artificial Intelligence Speech Recognition -- Exploration of Paths for Artificial Intelligence Technology to Promote Economic Development -- Influence of RPA Financial Robot on Financial Accounting and its Countermeasures -- Application of Artificial Intelligence Technology in English Online Learning Platform -- Spectral Identification Model of NIR Origin Based on Deep Extreme Learning Machine -- Frontier Application and Development Trend of Artificial Intelligence in New Media in the AI Era -- Analysis on the Application of Machine Learning Stock Selection Algorithm in the Financial Field -- Default Risk Prediction Based on Machine Learning under Big Data Analysis Technology -- Application of Intelligent Detection Technology and Machine Learning Algorithm in Music Intelligent System -- Application of 3D Computer Aided System in Dance Creation and Learning -- Data Selection and Machine Learning Algorithm Application under the Background of Big Data. . |
Record Nr. | UNINA-9910523725603321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022) / / edited by Aboul Ella Hassanien, Rawya Y. Rizk, Václav Snášel, Rehab F. Abdel-Kader |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
Descrizione fisica | 1 online resource (708 pages) |
Disciplina |
006.3
006.31 |
Collana | Lecture Notes on Data Engineering and Communications Technologies |
Soggetto topico |
Computational intelligence
Artificial intelligence Big data Engineering - Data processing Computational Intelligence Artificial Intelligence Big Data Data Engineering |
ISBN | 3-031-03918-1 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intro -- Preface -- Organization -- Honorary Chair -- General Chairs -- Co-chairs -- International Advisory Board -- Publication Chair -- Program Chairs -- Publicity Chairs -- Technical Program Committee -- Local Arrangement Chairs -- Contents -- Deep Learning and Applications -- Plant Leaf Diseases Detection and Identification Using Deep Learning Model -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Experimental Results -- 5 Conclusions -- References -- Reinforcement Learning for Developing an Intelligent Warehouse Environment -- 1 Introduction -- 2 Machine Learning Techniques -- 3 Results and Discussion -- 4 Conclusion and Future Research -- References -- A Low-Cost Multi-sensor Deep Learning System for Pavement Distress Detection and Severity Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Overall System Architecture -- 3.2 Deep Learning Distress Detection -- 3.3 Dataset and Training Information -- 3.4 Projection onto the Depth 3D Point Cloud and ROI Filtering -- 4 Case Study: Pothole Severity Classification -- 5 Experimental Results -- 5.1 Results for the Distress Detection -- 5.2 Results for Pothole Severity Classification -- 6 Conclusion -- References -- An Intrusion Detection Model Based on Deep Learning and Multi-layer Perceptron in the Internet of Things (IoT) Network -- 1 Introduction -- 2 Related Work -- 2.1 Multi Agent Systems for IDS -- 2.2 Fuzzy Systems for IDS -- 2.3 Game Theory Models for IDS -- 3 Architecture of the Proposed Intrusion Detection System -- 3.1 Pre-processing and Feature Engineering -- 3.2 Deep Learning Layer -- 3.3 Evaluation Layer -- 4 The Experimental Results -- 5 Comparison Between Proposed Models and the Others -- 6 Conclusion -- References -- Transfer Learning and Recurrent Neural Networks for Automatic Arabic Sign Language Recognition -- 1 Introduction.
2 Related Work -- 3 Arabic Sign Language Dataset -- 4 Methodology -- 4.1 Prepare the Dataset -- 4.2 Extract the Spatial Features -- 4.3 Extract the Temporal Features -- 4.4 Video Augmentation -- 5 Experimental and Results -- 5.1 Experiment Settings -- 5.2 Models Results -- 6 Conclusion and Future Works -- References -- Robust Face Mask Detection Using Local Binary Pattern and Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Experimental Results -- 5 Conclusion -- References -- Steganography Adaptation Model for Data Security Enhancement in Ad-Hoc Cloud Based V-BOINC Through Deep Learning -- 1 Introduction -- 1.1 Ad-Hoc Cloud Computing -- 1.2 Deep Steganography -- 1.3 Contribution -- 1.4 Paper Organization -- 2 Literature Review -- 3 Proposed Solution -- 4 Experiment -- 5 Discussion and Analysis -- 6 Conclusion -- References -- Performance of Different Deep Learning Models for COVID-19 Detection -- 1 Introduction -- 2 Deep Learning (DL) -- 2.1 The DL-Algorithms Steps in COVID-19 Diagnosis -- 2.2 DL-Models for COVID-19 Detection -- 3 Discussion -- 4 Conclusion -- References -- Deep Learning-Based Apple Leaves Disease Identification Approach with Imbalanced Data -- 1 Introduction -- 2 Basics and Background -- 2.1 Data Imbalance -- 2.2 Convolutional Neural Networks -- 2.3 Transfer Learning -- 3 The Proposed Approach -- 3.1 Dataset Description -- 3.2 Data Preprocessing Phase -- 3.3 Training Phase -- 3.4 Evaluation Phase -- 4 Experimental Results and Analysis -- 4.1 Data Imbalance Problem -- 4.2 Data Augmentation -- 4.3 Setup of the Experiment -- 4.4 Evaluation of the Model -- 5 Conclusion and Future Work -- References -- Commodity Image Retrieval Based on Image and Text Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Image and Text Feature Fusion -- 3.2 Target Function -- 4 Experiment -- 4.1 Evaluation Metrics. 4.2 Datasets -- 4.3 Experimental Details -- 4.4 Experimental Results and Analysis -- 5 Conclusion -- References -- Machine Learning Technologies -- Artificial Intelligence Based Solutions to Smart Warehouse Development: A Conceptual Framework -- 1 Introduction -- 2 SWOT Analysis -- 2.1 Strengths -- 2.2 Weaknesses -- 2.3 Opportunities -- 2.4 Threats -- 3 Proposed Solutions and Current Approaches -- 3.1 WO Strategy (Improve): Testbed as a Trial for Investment Decision -- 3.2 WO Strategy (Improve): AI-Powered Solutions -- 3.3 SO Strategy (Attack): AI Resource Development -- 4 Conclusions and Future Research -- References -- Long-Short Term Memory Model with Univariate Input for Forecasting Individual Household Electricity Consumption -- 1 Introduction -- 2 Related Works -- 3 Deep Learning Models for Load Forecasting -- 3.1 LSTM and LSTM-ED Neural Networks -- 3.2 CNN-LSTM Neural Networks -- 3.3 GRU Neural Networks -- 3.4 BiLSTM Neural Networks -- 3.5 ConvLSTM Neural Networks -- 4 Results and Discussion -- 4.1 Dataset Description -- 4.2 Evaluation Metrics -- 4.3 Prediction Results of ConvLSTM -- 4.4 Discussion of the Forecasting Models -- 5 Conclusion and Future Work -- References -- DNA-Binding-Proteins Identification Based on Hybrid Features Extraction from Hidden Markov Model -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Encoding -- 2.3 Framing -- 2.4 Hybrid Visual HMM Structure -- 2.5 Features Extraction -- 2.6 Classifier -- 3 Results and Discussions -- 4 Conclusions -- References -- Machine Learning Based Mobile Applications for Cardiovascular Diseases (CVDs) -- 1 Introduction -- 2 ML Based m-Health for CVDs -- 3 Characteristics of the Commercially Available CVDs Mobile Applications -- 4 Future Requirements -- 5 Conclusion -- References -- Regression Analysis for Remaining Useful Life Prediction of Aircraft Engines. 1 Introduction -- 2 Related Work -- 3 Aircraft Engine System -- 4 Proposed Model for Predicting the RUL -- 5 Experimental Results and Discussion -- 6 Conclusion and Future Work -- References -- Applying Machine Learning Technology to Perform Automatic Provisioning of the Optical Transport Network -- 1 Introduction -- 2 The Challenges in the Current Model of the Supervision of the OTN -- 3 Proposed Model for the Automatic Provision of the OTN -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Robo-Nurse Healthcare Complete System Using Artificial Intelligence -- 1 Introduction -- 1.1 Related Work -- 2 Research Method -- 2.1 Software Implementation -- 2.2 Hardware Implementation -- 2.3 External Design Implementation -- 3 Results and Discussions -- 4 Conclusion -- References -- Resolving Context Inconsistency Approach Based on Random Forest Tree -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 IoT Data Collection Phase -- 3.2 Context Inconsistency Validator -- 3.3 Best Resolution Selection -- 3.4 Random Forest Tree -- 4 Experimental Results and Evaluations -- 5 Conclusion and Future Directions -- References -- Arduino Line Follower Using Fuzzy Logic Control -- 1 Introduction -- 2 Methodology -- 2.1 Lab Simulation -- 2.2 The ATmega328p Microcontroller -- 2.3 Voltage Regulator -- 2.4 Circuit Diagram Explanation -- 2.5 Microcontroller-Motor Driver IC Interface -- 2.6 Microcontroller-IR Sensor Module Interface -- 2.7 Microcontroller-Variable Resistor Interface -- 2.8 Arduino IDE Interface with Microcontroller -- 3 Summary of Methodology -- 4 Physical Modeling -- 4.1 Block Diagram -- 4.2 Flow Chart -- 4.3 Working Principle -- 5 Result and Analysis -- 6 Conclusion -- References -- Evaluating Adaptive Facade Performance in Early Building Design Stage: An Integrated Daylighting Simulation and Machine Learning. 1 Introduction -- 2 Related Works -- 3 Building as a Machine and Machine Learning in Architecture -- 4 Adaptive Facade -- 5 Methodology -- 5.1 Data Collection: Available Forms of Kinetic Façade Systems -- 5.2 Data Preparation: Applying System Possibility Scores -- 5.3 Data Exploration and Case Study Setup -- 5.4 Prediction Stage: Applying the KNN Algorithm as a Selective Filter -- 6 Systems Modeling and Simulation -- 7 Results and Discussion -- 8 Conclusion -- References -- LTE Downlink Scheduling with Soft Policy Gradient Learning -- 1 Introduction -- 2 Downlink Resource Allocation in LTE -- 3 Related Work -- 4 DSPG Scheduler: The Proposed Scheduling Algorithm -- 4.1 Problem Statement -- 4.2 Model Design -- 5 Simulation Implementation and Results -- 6 Conclusions -- References -- Predicting the Road Accidents Severity Using Artificial Neural Network -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 The Proposed Methodology -- 5 Results and Discussions -- 5.1 Attributes vs Accident Severity -- 5.2 Accident Severity Prediction Results -- 6 Conclusion -- References -- Predicting the Intention to Use Audi and Video Teaching Styles: An Empirical Study with PLS-SEM and Machine Learning Models -- 1 Introduction -- 2 Theoretical Framework -- 2.1 Technology Acceptance Model (TAM) -- 2.2 Flow Theory -- 2.3 Virtual Reality Attributes -- 3 Research Methodology -- 3.1 Data Collection -- 3.2 Personal/Demographic Information -- 3.3 Study Instrument -- 3.4 Survey Structure -- 4 Findings and Discussion -- 4.1 Data Analysis -- 4.2 Convergent Validity -- 4.3 Discriminant Validity -- 4.4 Hypotheses Testing Using PLS-SEM -- 4.5 Hypothesis Testing Using Machine Learning Algorithms -- 5 Discussion of Results -- References -- Intellgenet Systems and Applications. Immunity of Signals Transmission Using Secured Unequal Error Protection Scheme with Various Packet Format. |
Record Nr. | UNINA-9910561300503321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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Advanced Analytics in Power BI with R and Python : Ingesting, Transforming, Visualizing / / by Ryan Wade |
Autore | Wade Ryan |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 |
Descrizione fisica | 1 online resource (XLVI, 391 p. 84 illus.) |
Disciplina | 001.4226028566 |
Soggetto topico |
Microsoft software
Microsoft .NET Framework Quantitative research Big data Microsoft Data Analysis and Big Data Big Data |
ISBN | 1-4842-5829-0 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Part I. Creating Custom Data Visualizations using R -- 1. The Grammar of Graphics -- 2. Creating R custom visuals in Power BI using ggplot2 -- Part II. Ingesting Data into the Power BI Data Model using R and Python -- 3. Reading CSV Files -- 4. Reading Excel Files -- 5. Reading SQL Server Data -- 6. Reading Data into the Power BI Data Model via an API -- Part III. Transforming Data using R and Python.-7. Advanced String Manipulation and Pattern Matching -- 8. Calculated Columns using R and Python -- Part IV. Machine Learning & AI in Power BI using R and Python -- 9. Applying Machine Learning and AI to your Power BI Data Models -- 10. Productionizing Data Science Models and Data Wrangling Scripts. . |
Record Nr. | UNINA-9910427050203321 |
Wade Ryan
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 | ||
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Lo trovi qui: Univ. Federico II | ||
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Advanced Applications of Blockchain Technology / / edited by Shiho Kim, Ganesh Chandra Deka |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
Descrizione fisica | 1 online resource (X, 278 p. 93 illus., 58 illus. in color.) |
Disciplina | 006.3 |
Collana | Studies in Big Data |
Soggetto topico |
Computational intelligence
Computer security Big data Computational Intelligence Systems and Data Security Big Data Privacy |
ISBN | 981-13-8775-3 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction to Blockchain Technology and IoT -- IoT, AI, and Blockchain: Implementation perspectives -- Blockchain Technologies for IoT -- Blockchain Technology Use Cases -- Blockchain meets CyberSecurity: Security, Privacy, Challenges and Opportunity -- On the Role of Blockchain Technology in Internet of Things -- Blockchain of Things (BCoT): The Fusion of Blockchain and IoT Technologies -- Blockchain Architecture -- Authenticating IoT Devices with Blockchain -- Security & Privacy Issues of Block chain Technology -- Supply Chain Management in Agriculture Using Blockchain and IoT -- Blockchain Technologies and Artificial Intelligence -- Blockchain Hands on for Developing Genesis Block. |
Record Nr. | UNINA-9910484022203321 |
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
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Lo trovi qui: Univ. Federico II | ||
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